UNICEF Data Story: Global Child Vulnerability & DTP Vaccination

Spring 2025 • BAA1030 Data Analytics & Storytelling (20074)
Student: Isha Tanwar (ID 48614)
Programme: MSc in Management (Strategy)

Note

Acknowledgement
Thanks to Prof. Dr. Damien Dupré for his unwavering guidance and support.

Introduction

This report uses UNICEF & World Bank data (2010–2024) to build a Composite Vulnerability Index (CVI) combining economic (GDP), health (DTP vaccination & life expectancy), and social (orphanhood) indicators. Fully interactive charts reveal disparities, trends, and policy levers for SDG 3 and SDG 10.

Code 
import pandas as pd
import plotly.express as px
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression

# 1) Load & preprocess data
df = pd.read_csv("merged_final.csv")
df["OrphanRate_per_1000"] = (
    df["OrphanCount"] / df["Population, total"]
) * 1000
df = df.rename(columns={
    "GDP per capita (constant 2015 US$)": "GDP",
    "Life expectancy at birth, total (years)": "LifeExpectancy"
})

# 2) Set year parameter (Quarto injects params; Jupyter fallback)
try:
    year = params.year
except NameError:
    year = 2022   # ← use any test year here when running interactively
Executive Summary
  • $1 000 GDP → + 0.22 pp DTP (R²≈0.08).
  • < 60 % DTP in Sub-Saharan Africa & South Asia.
  • Top CVI (2022): Somalia 72.5, Angola 67.7, Nigeria 67.6.
  • Orphans: Nigeria 13.9 M, DR Congo 5.8 M, Pakistan 5.8 M, Indonesia 5.7 M, Ethiopia 3.0 M.
  • Sim: + 12 % GDP → + 4–8 pp DTP in vulnerable states.

1. GDP vs DTP Coverage

Code 
fig1 = px.scatter(
    df.query("Year==@year").dropna(subset=["GDP","DTP"]),
    x="GDP", y="DTP",
    size="Population, total", color="Country",
    hover_name="Country",
    log_x=True, size_max=60,
    title=f"GDP per Capita vs DTP Coverage ({year})",
    labels={"GDP":"GDP per Capita (USD)","DTP":"DTP Coverage (%)"}
)
fig1.update_layout(margin=dict(l=40,r=0,t=50,b=30))
fig1.show()

Insight: Wealth explains about 8 % of the variation in DTP coverage—economics matter but other factors are also critical.

2. Top 10 Orphanhood Burden

Code 
top10 = df.query("Year==@year").nlargest(10,"OrphanCount").assign(
    Orphans_M=lambda d: d["OrphanCount"]/1e6
)
fig2 = px.bar(
    top10, x="Orphans_M", y="Country", orientation="h",
    hover_data={"Orphans_M":":.1f"}, labels={"Orphans_M":"Orphans (M)"},
    title=f"Top 10 Countries by Orphanhood ({year})"
)
fig2.update_layout(
    yaxis={'categoryorder':'total ascending'},
    margin=dict(l=100,r=0,t=50,b=30)
)
fig2.show()

Insight: Five countries (Nigeria, DR Congo, Pakistan, Indonesia, Ethiopia) account for over one-third of all orphans globally.

5. Animated Global DTP Coverage Map

Code 
fig5 = px.choropleth(
    df.dropna(subset=["DTP"]), locations="Country",
    locationmode="country names", color="DTP",
    animation_frame="Year", range_color=[0,100],
    color_continuous_scale="Viridis",
    title="Global DTP Coverage (2010–2024)"
)
fig5.update_layout(
    geo=dict(projection_type="natural earth"),
    margin=dict(l=0,r=0,t=50,b=0)
)
fig5.show()

Insight: The slider reveals how high-income regions sustain > 95 % coverage, while many low-income areas lag below 60 %.

6. Composite Vulnerability Index (CVI)

Code 
valid = df.dropna(subset=["GDP","DTP","LifeExpectancy","OrphanRate_per_1000"])
valid = valid[valid["Year"]<=year]
inv = pd.DataFrame({
    "DTP":100-valid["DTP"],
    "GDP":valid["GDP"].max()-valid["GDP"],
    "LE":valid["LifeExpectancy"].max()-valid["LifeExpectancy"],
    "OR":valid["OrphanRate_per_1000"]
})
valid["CVI"] = MinMaxScaler((0,100)).fit_transform(inv).mean(axis=1)
c = valid.query("Year==@year").nlargest(10,"CVI")
avg = valid.query("Year==@year")["CVI"].mean()

fig6 = px.bar(
    c, x="CVI", y="Country", orientation="h",
    hover_data={"CVI":":.1f"},
    title=f"Child Vulnerability Index Top 10 ({year})",
    labels={"CVI":"CVI (0–100)"}
)
fig6.add_vline(x=avg, line_dash="dash", annotation_text="Average", annotation_position="top right")
fig6.update_layout(
    yaxis={'categoryorder':'total ascending'},
    margin=dict(l=100,r=0,t=50,b=30)
)
fig6.show()

Insight: Highest CVI countries (Somalia, Angola, Nigeria) need integrated economic, health, and protection programs.

7. Policy Simulator: + 12 % GDP → DTP

Code 
from sklearn.linear_model import LinearRegression

# 1) Train on GDP → DTP up to selected year
train = df.query("Year <= @year").dropna(subset=["GDP","DTP"])
model = LinearRegression().fit(train[["GDP"]], train["DTP"])

# 2) Take your top-CVI DataFrame `c` (from chunk viz6-cvi)
sim = c.copy()
sim["GDP2"] = sim["GDP"] * 1.12

# 3) Prepare X for prediction: must have column name "GDP"
X_pred = sim[["GDP2"]].rename(columns={"GDP2":"GDP"})
sim["DTP2"] = model.predict(X_pred)

# 4) Melt for plotting
sim_melt = sim.melt(
    id_vars="Country",
    value_vars=["DTP","DTP2"],
    var_name="Scenario",
    value_name="Coverage"
)

# 5) Plot interactive bar
import plotly.express as px
fig7 = px.bar(
    sim_melt,
    x="Coverage", y="Country",
    color="Scenario",
    orientation="h",
    title=f"Simulated DTP Coverage with +12 % GDP ({year})",
    labels={"Coverage":"Coverage %","Scenario":"Scenario"}
)
fig7.update_layout(
    yaxis={'categoryorder':'total ascending'},
    margin=dict(l=100,r=0,t=50,b=30)
)
fig7.show()

Insight: A modest GDP increase could boost immunisation by 4–8 percentage points in the most vulnerable countries—powerful evidence for targeted economic support.

SDG Alignment & Recommendations

SDG 3: Good Health & Wellbeing

Invest in mobile clinics & outreach.

SDG 10: Reduced Inequalities

Direct fiscal transfers to high-CVI nations.
Note

Next Steps Checklist

References

UNICEF (2024) Child Vulnerability Indicators. Available at: https://data.unicef.org/ (Accessed: 15 April 2025).

World Bank (2024) World Development Indicators. Available at: https://data.worldbank.org/ (Accessed: 15 April 2025).

United Nations (2023) Sustainable Development Goals. Available at: https://sdgs.un.org/goals (Accessed: 15 April 2025).